Model comparison
Llama-3.3-70B-Instruct vs Qwen3.7 Plus
Qwen3.7 Plus is the stronger model overall, scoring 45.0 to 30.3 on the Noometry Index. Llama-3.3-70B-Instruct costs 4.5× less per token, which makes it the better buy when Qwen3.7 Plus's lead doesn't matter for your workload.
Last verified . 24 shared benchmarks.
Meta
Alibaba (Qwen)Summary
- They share 24 benchmarks with published results for both. Llama-3.3-70B-Instruct scores higher in 1 category and Qwen3.7 Plus in 8 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in math, where Qwen3.7 Plus leads 49.3 to 14.3.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 5.1% for Llama-3.3-70B-Instruct and 93.3% for Qwen3.7 Plus.
- Llama-3.3-70B-Instruct is cheaper at $0.10 / $0.32 per million input/output tokens, against $0.40 / $1.60 for Qwen3.7 Plus.
- Qwen3.7 Plus accepts more context: 1M tokens versus 128K.
- Llama-3.3-70B-Instruct has downloadable open weights; the other is API-only.
Side by side
| Llama-3.3-70B-Instruct | Qwen3.7 Plus | |
|---|---|---|
| Provider | Meta | Alibaba (Qwen) |
| Noometry Index | 30.3 | 45.0 |
| Released | 2024-12-06 | 2026-06-02 |
| Weights | Open | Proprietary |
| Context window | 128K | 1M |
| Max output | 4K | 131K |
| Input $ / M tokens | $0.10 | $0.40 |
| Output $ / M tokens | $0.32 | $1.60 |
| Results tracked | 43 | 32 |
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Category by category
Coding Qwen3.7 Plus leads
Llama-3.3-70B-Instruct: 30.6 (#284), Qwen3.7 Plus: 36.1 (#200)
| Benchmark | Llama-3.3-70B-Instruct | Qwen3.7 Plus |
|---|---|---|
| SciCode | 26% | 45.5% |
| LMArena Coding | 1268 | 1473 |
| FrontierCode | — | 10.2% |
| WeirdML | 14.4% | — |
| BigCodeBench Instruct | 46.9% | — |
| LiveBench Coding | 36.6% | — |
| BigCodeBench Complete | 57.5% | — |
Agentic & Tool Use Llama-3.3-70B-Instruct leads
Llama-3.3-70B-Instruct: 25.6 (#100), Qwen3.7 Plus: 21.2 (#130)
| Benchmark | Llama-3.3-70B-Instruct | Qwen3.7 Plus |
|---|---|---|
| Berkeley Function Calling Leaderboard | 31.9% | — |
| OSWorld 2.0 | — | 2.8% |
| BALROG | 23% | — |
Reasoning Qwen3.7 Plus leads
Llama-3.3-70B-Instruct: 13.7 (#325), Qwen3.7 Plus: 38.4 (#54)
| Benchmark | Llama-3.3-70B-Instruct | Qwen3.7 Plus |
|---|---|---|
| CritPt | 0% | 9.1% |
| LMArena Hard Prompts | 1257 | 1460 |
| DTBench | 59.5% | 84% |
| LMCA | 17.5% | 37.6% |
| Epoch Capabilities Index | 127.32 | 147.39 |
| SimpleBench | 19.9% | — |
| NYT Connections (extended) | — | 74.8% |
| Chess Puzzles | — | 24% |
| LiveBench Reasoning | 50.8% | — |
| Mystery Game Puzzles | — | 17% |
| LiveBench Data Analysis | 49.5% | — |
| ForecastBench | 58.6 | — |
| LiveBench | 50.2% | — |
Math Qwen3.7 Plus leads
Llama-3.3-70B-Instruct: 14.3 (#292), Qwen3.7 Plus: 49.3 (#47)
| Benchmark | Llama-3.3-70B-Instruct | Qwen3.7 Plus |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 5.1% | 93.3% |
| LMArena Math | 1268 | 1467 |
| FrontierMath (Tiers 1-3) | — | 34.4% |
| LiveBench Math | 42.2% | — |
| MATH Level 5 | 41.6% | — |
Knowledge Qwen3.7 Plus leads
Llama-3.3-70B-Instruct: 30.5 (#220), Qwen3.7 Plus: 54.9 (#46)
| Benchmark | Llama-3.3-70B-Instruct | Qwen3.7 Plus |
|---|---|---|
| GPQA Diamond | 47.4% | 87.9% |
| LMArena Expert | 1225 | 1467 |
| Confabulations | 22.8% | — |
| Vectara Hallucination Rate | 4.1% | — |
| MMLU | 86.3% | — |
Multimodal Not comparable
Llama-3.3-70B-Instruct: —, Qwen3.7 Plus: 41.2 (#30)
| Benchmark | Llama-3.3-70B-Instruct | Qwen3.7 Plus |
|---|---|---|
| LMArena Vision | — | 1281 |
| LMArena Document | — | 1444 |
Multilingual Qwen3.7 Plus leads
Llama-3.3-70B-Instruct: 40.1 (#215), Qwen3.7 Plus: 55.0 (#36)
| Benchmark | Llama-3.3-70B-Instruct | Qwen3.7 Plus |
|---|---|---|
| LMArena Non-English | 1236 | 1445 |
| LMArena Chinese | 1217 | 1510 |
| LMArena French | 1280 | 1471 |
| LMArena German | 1251 | 1471 |
| LMArena Japanese | 1149 | 1411 |
| LMArena Korean | 1142 | 1416 |
| LMArena Russian | 1252 | 1457 |
| LMArena Spanish | 1269 | 1453 |
Instruction Following Qwen3.7 Plus leads
Llama-3.3-70B-Instruct: 71.3 (#152), Qwen3.7 Plus: 75.9 (#49)
| Benchmark | Llama-3.3-70B-Instruct | Qwen3.7 Plus |
|---|---|---|
| LMArena Instruction Following | 1242 | 1439 |
| LiveBench Instruction Following | 82.7% | — |
Long Context Qwen3.7 Plus leads
Llama-3.3-70B-Instruct: 26.4 (#290), Qwen3.7 Plus: 44.5 (#61)
| Benchmark | Llama-3.3-70B-Instruct | Qwen3.7 Plus |
|---|---|---|
| LMArena Longer Query | 1256 | 1454 |
| Fiction.LiveBench | 33.3% | — |
Writing & Preference Qwen3.7 Plus leads
Llama-3.3-70B-Instruct: 47.8 (#203), Qwen3.7 Plus: 64.5 (#54)
| Benchmark | Llama-3.3-70B-Instruct | Qwen3.7 Plus |
|---|---|---|
| LMArena Text | 1274 | 1454 |
| LMArena Creative Writing | 1250 | 1439 |
| LMArena Multi-Turn | 1279 | 1459 |
| LiveBench Language | 39.2% | — |
Frequently asked questions
Is Llama-3.3-70B-Instruct better than Qwen3.7 Plus?
Qwen3.7 Plus is the stronger model overall, scoring 45.0 to 30.3 on the Noometry Index. Llama-3.3-70B-Instruct costs 4.5× less per token, which makes it the better buy when Qwen3.7 Plus's lead doesn't matter for your workload.
Which is cheaper, Llama-3.3-70B-Instruct or Qwen3.7 Plus?
Llama-3.3-70B-Instruct is cheaper. It lists at $0.10 per million input tokens and $0.32 per million output tokens; Qwen3.7 Plus lists at $0.40 and $1.60.
Is Llama-3.3-70B-Instruct or Qwen3.7 Plus better for coding?
Qwen3.7 Plus scores higher on coding benchmarks: 36.1 versus 30.6 in the Noometry coding category.
Which has the bigger context window?
Qwen3.7 Plus does, with 1M tokens against 128K.
How many benchmarks do Llama-3.3-70B-Instruct and Qwen3.7 Plus share?
24 benchmarks have published results for both models. Llama-3.3-70B-Instruct has 43 scored results on Noometry and Qwen3.7 Plus has 32.